Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Federated graph neural networks: Overview, techniques, and challenges
Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …
due to their capability to progress with graph data and have been widely used in practical …
Green edge AI: A contemporary survey
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …
Defending batch-level label inference and replacement attacks in vertical federated learning
In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …
different parties, it has been shown that sample-level gradient information can be exploited …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
Flexifed: Personalized federated learning for edge clients with heterogeneous model architectures
Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …
the world's web traffic, making a great data source for various machine learning (ML) …
Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients
Federated Learning (FL) trains a machine learning model on distributed clients without
exposing individual data. Unlike centralized training that is usually based on carefully …
exposing individual data. Unlike centralized training that is usually based on carefully …
A comprehensive survey of federated transfer learning: challenges, methods and applications
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …
participants to collaboratively train a centralized model with privacy preservation by …
Federated fine-tuning of billion-sized language models across mobile devices
Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …
Federated learning-empowered mobile network management for 5G and beyond networks: From access to core
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …
integrated communication and computing platform that will enable multipurpose and …